Power and stability properties of resampling-based multiple testing procedures with applications to gene oncology studies

Computational and Mathematical Methods in Medicine
Dongmei Li, Timothy D Dye

Abstract

Resampling-based multiple testing procedures are widely used in genomic studies to identify differentially expressed genes and to conduct genome-wide association studies. However, the power and stability properties of these popular resampling-based multiple testing procedures have not been extensively evaluated. Our study focuses on investigating the power and stability of seven resampling-based multiple testing procedures frequently used in high-throughput data analysis for small sample size data through simulations and gene oncology examples. The bootstrap single-step minP procedure and the bootstrap step-down minP procedure perform the best among all tested procedures, when sample size is as small as 3 in each group and either familywise error rate or false discovery rate control is desired. When sample size increases to 12 and false discovery rate control is desired, the permutation maxT procedure and the permutation minP procedure perform best. Our results provide guidance for high-throughput data analysis when sample size is small.

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Citations

Jul 6, 2014·Frontiers in Oncology·Mahboobeh SafaeianMargaret M Madeleine
Jul 7, 2017·The Clinical Neuropsychologist·Jacqueline N ZadelaarHilde M Huizenga
Apr 14, 2019·Genes·Jin-Qi MaJia-Na Li
May 17, 2018·Molecular Medicine Reports·Shouhua ChenWenhua Zhao

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Datasets Mentioned

BETA
GSE7463

Software Mentioned

SAM
multitest
siggenes

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